CN110189334A - The medical image cutting method of the full convolutional neural networks of residual error type based on attention mechanism - Google Patents
The medical image cutting method of the full convolutional neural networks of residual error type based on attention mechanism Download PDFInfo
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Abstract
The present invention provides a kind of medical image cutting method of full convolutional neural networks of residual error type based on attention mechanism, pre-processes to medical image to be split；The full convolutional neural networks of residual error type based on attention mechanism are constructed, including characteristic pattern shrinks network, attention network, characteristic pattern and expands group of networks；The training set data input full convolutional neural networks of residual error type are trained the convolutional neural networks model after being learnt；Test set data are inputted into the convolutional neural networks model after gained study, carry out image segmentation, the image after being divided；Characteristic pattern is shunk the characteristics of image extracted in network using attention network and effectively passes to characteristic pattern expansion network by this method, it solves during image deconvolution, the problem of lacking the space characteristics of image, simultaneously attention network can also inhibit in low-level feature figure with the incoherent image-region of segmentation object, the redundancy of image is reduced, while also increasing the accuracy rate of image segmentation.
Description
Technical field
The medical image cutting method of the present invention relates to a kind of full convolutional neural networks of residual error type based on attention mechanism.
Background technique
Medical image segmentation is to determine that can medical image provide the critical issue of reliable basis in clinic diagnosis.Medicine
The development of image Segmentation Technology not only influences the development of other the relevant technologies in Medical Image Processing, such as visualization, Three-dimensional Gravity
It builds, and also occupies extremely important status in the analysis of Biomedical Image.In recent years, since deep learning algorithm exists
Application in medical image segmentation, Medical Image Segmentation Techniques achieve significant progress.Medical image segmentation is generally modeled as
More classification problems of pixel scale, target are that each pixel of image is divided into one of predefined multiple classifications.
Traditional medical image cutting method generally extracts the feature artificially designed and do again from the small window of image pixel neighborhood to be sentenced
Not, such as textural characteristics.Simultaneously, it is contemplated that the spatial dependence between image pixel, researcher is based on lower-level vision feature
Similitude constructs the characterization of more advanced global context, such as markov random file and condition random field.
Deep learning majority in medical image segmentation is the convolutional neural networks based on coder-decoder at present.It is this
It, will necessarily be encoded although once network structure can obtain preferable semantic segmentation as a result, still using encoding and decoding structure
Journey significantly reduces the spatial resolution of characteristic pattern, can not although restoring the original resolution of image in upper sampling process
What is avoided will cause the loss of spatial detail information.
It is interfered simultaneously to reduce the background area in image to target area, reducing picture redundancy is also in image segmentation
Key technology.It usually include a region of interesting extraction module in image segmentation.Utilize intensively connecting between neuron
It connects, interested target area is extracted from original image.However, this method leads to the mistake of computing resource and model parameter
Degree and redundancy use, for example, similar low-level features be cascaded in all models repeat extract.
The neural network structure that oneself has at present is to carry out certain change to network depth in the network structure that oneself has mostly
Into, by deepen the network number of plies network depth is deepened.Experiment shows the intensification for the network number of plies to a certain extent
The accuracy of network training is helped to improve, but will appear network performance when the number of plies increases to certain amount and degenerate, even make
The problem of disappearing at gradient.
The above problem is should to pay attention to and solve the problems, such as during medical image segmentation.
Summary of the invention
The medical image of the object of the present invention is to provide a kind of full convolutional neural networks of residual error type based on attention mechanism
Dividing method, solution is existing in the prior art during image deconvolution, and the loss of spatial detail information causes shortage to be schemed
The space characteristics of picture also result in the problem of excessive and redundancy of computing resource and model parameter uses.
The technical solution of the invention is as follows:
A kind of medical image cutting method of the full convolutional neural networks of residual error type based on attention mechanism, including following step
Suddenly,
S1, medical image to be split is pre-processed, obtains training set data, verifying collection data and test set number
According to；
S2, residual error type full convolutional neural networks of the building based on attention mechanism, including characteristic pattern shrink network, attention
Network, characteristic pattern expand group of networks, wherein characteristic pattern shrinks network for the feature extraction to original input picture, obtains figure
As characteristic information；On the basis of characteristic pattern expands the image feature information that network is used to shrink characteristic pattern network offer, prediction
Segmented image identical with original image size size out；Attention network is used to shrinking every layer of characteristic pattern into image in network special
Sign passes to characteristic pattern expansion network；
S3, the training set data input full convolutional neural networks of residual error type are trained, the convolutional Neural after being learnt
Network model；
S4, by the convolutional neural networks model after study obtained by test set data input step S3, carry out image segmentation, obtain
Image after to segmentation.
Further, in step S2, it includes that layering is arranged from top to bottom and structure is four layers identical that characteristic pattern, which shrinks network,
Residual error convolution block and layering setting from top to bottom and the identical four layers of maximum pond layer of structure, four layers of residual error convolution block, that is, residual error volume
Block one, residual error convolution block two, residual error convolution block three, residual error convolution block four, the i.e. maximum pond layer one of four layers of maximum pond layer,
Maximum pond layer two, maximum pond layer three, maximum pond layer four, the input of residual error convolution block one is original input picture, residual error
Convolution block one passes through maximum pond layer one and is output to residual error convolution block two, residual error convolution block two, residual error convolution block three and residual error volume
The input of block four is output and original of the output of the residual error convolution block from upper one layer after maximum pond layer operation respectively
The characteristic pattern of beginning image averaging Chi Huahou merges；Residual error convolution block two and residual error convolution block three are defeated respectively after feature extraction
It arrives corresponding attention network out, while being output to after the operation of maximum pondization characteristic pattern and shrinking next layer of network residual
Poor convolution block, residual error convolution block four are output to corresponding attention network, while residual error convolution block four after feature extraction
The residual error convolution block five of characteristic pattern expansion network will be output to by maximum pond layer four.
Further, in step S2, it includes that layering is arranged from bottom to top and structure is five layers identical that characteristic pattern, which expands network,
Residual error convolution block, i.e. residual error convolution block five, residual error convolution block six, residual error convolution block seven, residual error convolution block eight and residual error convolution block
Nine, the input of residual error convolution block five shrinks the residual error convolution block four of network after the operation of maximum pond layer four from characteristic pattern
The input of output, residual error convolution block six, residual error convolution block seven, residual error convolution block eight and residual error convolution block nine comes from upper one layer of residual error
The merging of output of the convolution block Jing Guo deconvolution and the characteristic pattern by the output of attention network.
Further, characteristic pattern shrinks the structure of the residual error convolution block of network and the residual error convolution block of characteristic pattern expansion network
Identical, the input x of residual error convolution block is by identical convolution twice in succession, batch normalization Batch Normal, activation primitive
After relu, main output F (x) is obtained, input x also directly will be from input x and main output F by being directly connected to shortcut
(x) it is added, obtains final output F (x)+x.
Further, in step S2, there are two inputs for attention network tool, i.e., respectively from upper one layer of characteristic pattern expansion web
The residual error convolution block of network is shunk in the output of residual error convolution block and characteristic pattern in network, and the convolution of 1*1*1 is passed through in two inputs respectively
Then operation sums two input feature vector figures, the summed result pass through according to this relu activation primitive, 1*1*1 convolution operation,
Sampled result is finally multiplied with the characteristic pattern for shrinking network from characteristic pattern, is output to by sigmoild activation primitive, up-sampling
Destination layer.
Further, in step S1, medical image to be split is pre-processed, specifically,
S11, medical image to be split is formatted；
S12, the image after format conversion is normalized, is normalized to [0,1] section；Specifically, meter
The mean value and standard deviation for calculating whole set of data image are normalized the contrast of formula manipulation image by contrast, wherein contrast
Normalizing formula indicates are as follows:
I=(I-Mean)/Std (1)
Wherein, I indicates the contrast of image, and Mean indicates the mean value of image data, and Std indicates the standard of image data
Difference；
S13, the image after normalization is divided into training set data, verifying collection and test set data；
S14, multiscalization processing is carried out to training set data, respectively obtains the different image of sizes.
Further, in step S3, the training set data input full convolutional neural networks of residual error type is trained, are learned
Convolutional neural networks model after habit；Specifically,
S31, training set data is divided into m batches, and initializes convolution kernel weight and bias；
S32, in batches by the training set data input step S2 not being trained to build based on the residual of attention mechanism
The poor full convolutional neural networks of type；
S33, it will be carried out in training set data and the full convolutional neural networks of residual error type based on attention mechanism that build
It calculates, realizes the propagated forward of network training, export prediction probability figure；
S34, the error corresponded between Standard Segmentation picture in each layer prediction probability figure and training set data is calculated, calculated public
Formula are as follows:
It is classification corresponding to each pixel that wherein γ, which is 1.33, c,
Wherein, PicRepresent the probability that pixel i is predicted as c class, giCThe corresponding value of pixel i in Standard Segmentation image,It indicates
Pixel i is not belonging to the probability of c class,Then indicate that pixel i corresponds to 1-g in Standard Segmentation imageiCValue, N represents a figure
The total number of pixel as in, the value that the value of α is 0.7, β is 0.3, ε 1；
Residual error convolution block six, residual error convolution block seven, residual error convolution block eight the corresponding error calculation function of output be FTLc,
The corresponding error calculation function of the output of residual error convolution block nine is TIc；
S35, error calculation function is minimized using momentum stochastic gradient descent method, then utilizes the error calculation functional value
Carry out gradient calculating, to network parameter update when selection multistep learning rate strategy change learning rate, according to the number of iterations by
It is decrescence small；After the completion of network parameter updates, the image of verifying collection data is input in the network model trained, verifying is calculated
Collect the accuracy rate of image segmentation in data, if the network model after batch training concentrates accuracy rate to be higher than last batch in verifying
Otherwise the accuracy rate of training pattern, then the network model parameter after saving training are not saved into fixed disk file；Training set data
Each batch be respectively trained after the completion of, finally obtain study after convolutional neural networks model.
Further, in step S32, the different image input of four kinds of sizes in training set data is based on attention mechanism
The full convolutional neural networks of residual error type characteristic pattern shrink network corresponding level in.
The beneficial effects of the present invention are: the medical image of residual error type full convolutional neural networks of this kind based on attention mechanism
Characteristic pattern is shunk the characteristics of image extracted in network using attention network and effectively passes to characteristic pattern expansion by dividing method
The problem of network solves during image deconvolution, lacks the space characteristics of image, while attention network can also press down
With the incoherent image-region of segmentation object in low-level feature figure processed, the redundancy of image is reduced, while also increasing image point
The accuracy rate cut.It is solved when deepening network structure using residual error convolution block, is easy the gradient caused disappearance, network performance
The problem of degeneration.
Detailed description of the invention
Fig. 1 is the medical image segmentation side of residual error type full convolutional neural networks of the embodiment of the present invention based on attention mechanism
The flow diagram of method.
Fig. 2 is that full convolutional neural networks of residual error type in embodiment based on attention mechanism illustrate schematic diagram.
Wherein: in black arrow, conv is the convolution operation that convolution kernel size is 3*3, and bn is that batch normalizes, and relu is
Activation primitive.Green arrow represents convolution kernel and operates as the maximum pondization of 2x2.Orange arrows represent deconvolution operation.Orange arrow
In head, 2x2deconv represents convolution kernel and operates as the deconvolution of 2*2.Skip connection representative in dotted arrow is skipped
Connection, does not carry out other operations.Blue arrow represents convolution kernel as the convolution operation of 1x1, and activates letter by sigmoid
Number.The arrow-shaped pattern of grey represents attention network.Blue block diagram represents residual error convolution block.Multi-scale inputs
Multiple dimensioned input is represented, deep supervision represents multi-level error calculation.
Fig. 3 is that residual error convolution block illustrates schematic diagram in embodiment.Wherein relu is activation primitive, and bn is that batch normalizes
Operation, conv is convolution operation.X is input, is exported based on F (x), and F (x)+x is final output.
Fig. 4 is that attention network illustrates schematic diagram in embodiment.
Specific embodiment
The preferred embodiment that the invention will now be described in detail with reference to the accompanying drawings.
Embodiment
A kind of medical image cutting method of the full convolutional neural networks of residual error type based on attention mechanism, such as Fig. 1, including
Following steps:
S1, medical image to be split is pre-processed, obtains training set data, verifying collection data and test set
Data.
S11, medical image to be split is formatted.The medical image of original dcm format is converted
At the medical image of png format.
S12, the image after format conversion is normalized, is normalized to [0,1] section.
The mean value and standard deviation for calculating whole set of data image are normalized the contrast of formula manipulation image by contrast,
Wherein contrast normalization formula indicates are as follows:
I=(I-Mean)/Std (1)
Wherein, I indicates the contrast of image, and Mean indicates the mean value of image data, and Std indicates the standard of image data
Difference.
S13, by the image after normalization, 7:1:2 divides for training set data, verifying collection and test set data in proportion.
S14, multiscalization processing is carried out to training set data.At multiscalization specifically: by the figure after the completion of step S1
As carrying out average pondization operation, 1/2 size of original image size is obtained, averagely pondization operation is repeated and respectively obtains original image size
1/4, the image of 1/8 size finally respectively obtains four kinds of different images of size.
S2, residual error type full convolutional neural networks of the building based on attention mechanism, such as Fig. 2, including characteristic pattern contraction network,
Attention network, characteristic pattern expand group of networks, wherein and characteristic pattern contraction network is used for the feature extraction to original input picture,
Obtain image feature information；Characteristic pattern expands the basis that network is used to shrink characteristic pattern the image feature information that network provides
On, predict segmented image identical with original image size size；Attention network is used to every layer of characteristic pattern shrinking network
Middle characteristics of image passes to characteristic pattern expansion network.
S21, construction feature figure shrink network, for extracting the semantic feature of image.
Characteristic pattern shrink network include layering setting from top to bottom and the identical four layers of residual error convolution block of structure and from upper and
Lower leaf setting and the identical four layers of maximum pond layer of structure, four layers of residual error convolution block, that is, residual error convolution block one
ResConvBlock_1, two ResConvBlock_2 of residual error convolution block, three ResConvBlock_3 of residual error convolution block, residual error convolution
Four ResConvBlock_4 of block, four layers of maximum pond layer are one MaxPooling_1 of maximum pond layer, maximum pond layer two
MaxPooling_2, three MaxPooling_3 of maximum pond layer, four MaxPooling_4 of maximum pond layer, residual error convolution block one
Input is original input picture, and residual error convolution block one passes through maximum pond layer one and is output to residual error convolution block two, residual error convolution block
Two, the input of residual error convolution block three and residual error convolution block four is the output of the residual error convolution block from upper one layer respectively by maximum
Output and original image after the layer operation of pond be averaged Chi Huahou characteristic pattern merge；Residual error convolution block two and residual error convolution block three
After feature extraction, it is respectively outputted to corresponding attention network, while being output to feature after the operation of maximum pondization
Figure shrinks next layer of residual error convolution block of network, and residual error convolution block four is output to corresponding attention net after feature extraction
Network, while residual error convolution block four will also be output to the residual error convolution block five of characteristic pattern expansion network by maximum pond layer four.
The left-half of Fig. 2 is that the characteristic pattern of parted pattern shrinks network.It includes: residual that characteristic pattern shrinks network from top to bottom
Poor one ResConvBlock_1 of convolution block, maximum one MaxPooling_1 of pond layer, two ResConvBlock_2 of residual error convolution block,
Maximum two MaxPooling_2 of pond layer, residual error convolution block three
ResConvBlock_3, maximum three MaxPooling_3 of pond layer, four ResConvBlock_4 of residual error convolution block, most
Four MaxPooling_4 of great Chiization layer.
Wherein ResConvBlock_1, ResConvBlock_2, ResConvBlock_3, ResConvBlock_4 are residual
Poor convolution block, the number at residual error convolution block title end represent Fig. 2 left feature figure and shrink the elder generation occurred from top to bottom in network
Sequence afterwards.The network structure of residual error convolution block is as shown in figure 3, input x is normalized by identical convolution twice in succession, batch
After Batch Normal (BN), activation primitive relu, obtained main output F (x).X is inputted by being directly connected to shortcut
It will be directly added from input x with main output F (x), and obtain final output F (x)+x, remain the integrality of information, while
The ability to express of network is enhanced, the problem of gradient disappears in the case where deepening network is alleviated.MaxPooling_1,
MaxPooling_2, MaxPooling_3, MaxPooling_4 are the maximum pondization operation that convolution kernel is 2*2, for further
Feature is extracted, network parameter is reduced.The number at maximum pond action name end is represented shrinks in network in Fig. 2 left feature figure
The sequencing occurred from top to bottom.The left side of Fig. 2, the input that characteristic pattern shrinks the ResConvBlock_1 of network is original graph
The input of picture, ResConvBlock_2, ResConvBlock_3, ResConvBlock_4 is operated from upper one layer of maximum pondization
The characteristic pattern of the input picture of correspondingly-sized merges after output and Image Multiscaleization processing afterwards.
S22, construction feature figure path expander are used for forecast image semantic information.
It includes layering setting from bottom to top and the identical five layers of residual error convolution block of structure, i.e. residual error volume that characteristic pattern, which expands network,
It is five ResConvBlock_5 of block, six ResConvBlock_6 of residual error convolution block, seven ResConvBlock_7 of residual error convolution block, residual
Nine ResConvBlock_9 of poor eight ResConvBlock_8 of convolution block and residual error convolution block, residual error convolution block five
The input of ResConvBlock_5 shrinks four ResConvBlock_4 of residual error convolution block of network by maximum pond from characteristic pattern
Output after changing four MaxPooling_4 of layer operation, residual error convolution block six, residual error convolution block seven, residual error convolution block eight and residual error volume
Output of the input of block nine from upper one layer of residual error convolution block Jing Guo deconvolution and the characteristic pattern by the output of attention network
Merging.
The right half part of Fig. 2 is that the characteristic pattern of parted pattern expands network.It includes: residual error convolution block that characteristic pattern, which expands network,
Five ResConvBlock_5, DeConv_1, residual error convolution block six ResConvBlock_6, DeConv_2, residual error convolution block seven
ResConvBlock_7, DeConv_3, residual error convolution block eight ResConvBlock_8, DeConv_4.Wherein ResConvBlock_
5, ResConvBlock_6, ResConvBlock_7, ResConvBlock_8, ResConBlock_9 are residual error convolution block, structure
It is identical that the structure of residual error convolution block in network is shunk with characteristic pattern.The number at residual error convolution block title end is represented from the right side Fig. 1
Side appears in the sequencing of characteristic pattern expansion network from bottom to top.DeConv_1, DeConv_2, DeConv_3, DeConv_4
For deconvolution operation, the purpose of deconvolution is 2 times of dimension enlargement of the characteristics of image that will be inputted.The number at deconvolution title end
It represents deconvolution and operates the sequencing occurred from bottom to top on the right side of Fig. 2.The input of ResConvBlock_5 comes from feature
Figure shrinks the maximum pond layer MaxPooling_4 in network.ResConvBlock_6, ResConvBlock_7,
The input of ResConvBlock_8 merges from the characteristic pattern of upper one layer of deconvolution and respective corresponding attention network.And
And in expansion network, ResConvBlock_6, ResConvBlock_7, ResConvBlock_8, ResConvBlock_9's
Output has corresponded to a loss function all to evaluate current prediction effect.
S23, building attention network, are used for low-level feature figure region of interesting extraction, to region relevant to target
Image plays reinforcing effect, and the area image unrelated with target area plays the effect of inhibition.As shown in Figure 3.Individually paying attention to
In power network, the output of the residual error convolution block of network is shunk in output and characteristic pattern from upper layer residual error convolution block, the two works
It is the convolution operation that 1*1*1 is passed through in input, two input feature vector figures is summed, then summed result passes through relu according to this and activates letter
Number, the convolution operation of 1*1*1, sigmoild activation primitive, up-sampling, finally shrink the residual of network for sampled result and characteristic pattern
The characteristic pattern of poor convolution block is multiplied, and is output to destination layer.Wherein, in the full convolutional neural networks of residual error type based on attention mechanism
In from top to bottom exist three attention networks, attention network one, attention network two, attention network three, attention net
The destination layer of network one is characterized the residual error convolution block eight of figure expansion network, and the destination layer of attention network two is characterized figure expansion web
The residual error convolution block seven of network, the destination layer of attention network three are characterized the residual error convolution block six of figure expansion network.
S3, the training set data input full convolutional neural networks of residual error type are trained, the convolutional Neural after being learnt
Network model.
S31, training set data is divided into m batches, and initializes convolution kernel weight and bias.
S32, the residual error type based on attention mechanism in batches building the training set data not being trained to input are complete
Convolutional neural networks.Wherein as shown in Figure 1, in the corresponding level of the different image input segmentation network of four kinds of sizes, to be next
The feature extraction of step provides the spatial information of more image, semantics.
S33, it will be carried out in training set data and the full convolutional neural networks of residual error type based on attention mechanism that build
It calculates, realizes the propagated forward of network training, export prediction probability figure.
S34, the error corresponded between Standard Segmentation picture in each layer prediction probability figure and training set data is calculated, calculated public
Formula are as follows:
It is classification corresponding to each pixel that wherein γ, which is 1.33, c,
Wherein, PicRepresent the probability that pixel i is predicted as c class, giCThe corresponding value of pixel i in Standard Segmentation image,It indicates
Pixel i is not belonging to the probability of c class,Then indicate that pixel i corresponds to 1-g in Standard Segmentation imageiCValue, N represents a figure
The total number of pixel as in, the value that the value of α is 0.7, β is 0.3, ε 1；
Residual error convolution block in characteristic pattern expansion network, ResConvBlock_6, ResConvBlock_7,
ResConvBlock_8,ResConvBlock_9.Each piece of output has the Standard Segmentation picture of correspondingly-sized and corresponding
Error calculation function, wherein the error calculation function of ResConvBlock_6, ResConvBlock_7, ResConvBlock_8 be
FTLc, the corresponding error calculation function of the output of the last one ResConvBlock_9 is TIc。
S35, error calculation function is minimized using momentum stochastic gradient descent method, then utilizes the error calculation functional value
Carry out gradient calculating, to network parameter update when selection multistep learning rate strategy change learning rate, according to the number of iterations by
It is decrescence small；After the completion of network parameter updates, the image of verifying collection data is input in the network model trained, verifying is calculated
Collect the accuracy rate of image segmentation in data, if the network model after batch training concentrates accuracy rate to be higher than last batch in verifying
Otherwise the accuracy rate of training pattern, then the network model parameter after saving training are not saved into fixed disk file；Training set data
Each batch be respectively trained after the completion of, finally obtain study after convolutional neural networks model.
S4, by the convolutional neural networks model after study obtained by test set data input step S3, carry out image segmentation, obtain
Image after to segmentation.
The medical image cutting method of residual error type full convolutional neural networks of this kind based on attention mechanism is instructed by selection
Practice data set, validation data set and test data set, and above-mentioned image is pre-processed；It constructs and trains and shunk by characteristic pattern
The Image Segmentation Model that network, attention network, characteristic pattern expansion network are constituted.Network is wherein shunk to be mainly responsible for original defeated
Enter the feature extraction of image, and expands network and be then responsible for finally predicting on the basis of shrinking the characteristics of image that network provides
Segmented image identical with original image size size.Attention network is then responsible for having characteristics of image in every layer of contraction network
Effect passes to contraction network.The multiple batches of training set data point are put into training in network, the weight parameter after saving convergence.
The medical image cutting method of residual error type full convolutional neural networks of this kind based on attention mechanism, combines attention
Power mechanism and residual error type network improve the performance of segmentation network in the advantage of medical image segmentation.Solves existing segmentation side
Method divides the problems such as accuracy rate is low, image space detailed information is lost.
The medical image cutting method of residual error type full convolutional neural networks of this kind based on attention mechanism, passes through attention
Network is enhanced or is inhibited to low-level feature figure, is reduced low-level feature figure and is transmitted to existing redundancy letter when high-level characteristic figure
Breath.The accuracy rate of image segmentation is also increased simultaneously.By residual error convolution block reduce because network depth increase caused by gradient
It disappears, solves the problems, such as that network performance declines.
Claims (8)
1. a kind of medical image cutting method of the full convolutional neural networks of residual error type based on attention mechanism, it is characterised in that:
Include the following steps,
S1, medical image to be split is pre-processed, obtains training set data, verifying collection data and test set data；
S2, residual error type full convolutional neural networks of the building based on attention mechanism, including characteristic pattern shrink network, attention net
Network, characteristic pattern expand group of networks, wherein characteristic pattern shrinks network for the feature extraction to original input picture, obtains image
Characteristic information；On the basis of characteristic pattern expands the image feature information that network is used to shrink characteristic pattern network offer, predict
Segmented image identical with original image size size；Attention network is used to every layer of characteristic pattern shrinking characteristics of image in network
Pass to characteristic pattern expansion network；
S3, the training set data input full convolutional neural networks of residual error type are trained, the convolutional neural networks after being learnt
Model；
S4, by the convolutional neural networks model after study obtained by test set data input step S3, carry out image segmentation, divided
Image after cutting.
2. the medical image segmentation side of the full convolutional neural networks of residual error type based on attention mechanism as described in claim 1
Method, it is characterised in that: in step S2, it includes layering setting from top to bottom and the identical four layers of residual error of structure that characteristic pattern, which shrinks network,
Convolution block and layering setting from top to bottom and the identical four layers of maximum pond layer of structure, four layers of residual error convolution block, that is, residual error convolution block
One, residual error convolution block two, residual error convolution block three, residual error convolution block four, four layers of maximum pond layer are maximum pond layer one, maximum
Pond layer two, maximum pond layer three, maximum pond layer four, the input of residual error convolution block one is original input picture, residual error convolution
Block one passes through maximum pond layer one and is output to residual error convolution block two, residual error convolution block two, residual error convolution block three and residual error convolution block
Four input is output and original graph of the output of the residual error convolution block from upper one layer after maximum pond layer operation respectively
As the characteristic pattern of average Chi Huahou merges；Residual error convolution block two and residual error convolution block three are respectively outputted to after feature extraction
Corresponding attention network, at the same by maximum pondization operation after be output to characteristic pattern shrink network next layer of residual error volume
Block, residual error convolution block four are output to corresponding attention network after feature extraction, while residual error convolution block four also will be through
Cross the residual error convolution block five that maximum pond layer four is output to characteristic pattern expansion network.
3. the medical image segmentation side of the full convolutional neural networks of residual error type based on attention mechanism as claimed in claim 2
Method, it is characterised in that: in step S2, it includes layering setting from bottom to top and the identical five layers of residual error of structure that characteristic pattern, which expands network,
Convolution block, i.e. residual error convolution block five, residual error convolution block six, residual error convolution block seven, residual error convolution block eight and residual error convolution block nine, it is residual
The input of poor convolution block five shrinks output of the residual error convolution block four of network after the operation of maximum pond layer four from characteristic pattern,
The input of residual error convolution block six, residual error convolution block seven, residual error convolution block eight and residual error convolution block nine comes from upper one layer of residual error convolution
The merging of output of the block Jing Guo deconvolution and the characteristic pattern by the output of attention network.
4. the medical image segmentation side of the full convolutional neural networks of residual error type based on attention mechanism as claimed in claim 3
Method, it is characterised in that: characteristic pattern shrinks the structure phase of the residual error convolution block and the residual error convolution block of characteristic pattern expansion network of network
Together, the input x of residual error convolution block is by identical convolution twice in succession, batch normalization Batch Normal, activation primitive
After relu, main output F (x) is obtained, input x also directly will be from input x and main output F by being directly connected to shortcut
(x) it is added, obtains final output F (x)+x.
5. the medical image segmentation side of the full convolutional neural networks of residual error type based on attention mechanism as claimed in claim 3
Method, it is characterised in that: in step S2, there are two inputs for attention network tool, i.e., expand network respectively from upper one layer of characteristic pattern
In residual error convolution block output and characteristic pattern shrink network residual error convolution block, two input respectively by 1*1*1 convolution grasp
Make, then two input feature vector figures summed, the summed result pass through according to this relu activation primitive, 1*1*1 convolution operation,
Sampled result is finally multiplied with the characteristic pattern for shrinking network from characteristic pattern, is output to by sigmoild activation primitive, up-sampling
Destination layer.
6. the medical image of the residual error type full convolutional neural networks as described in any one in claim 1-5 based on attention mechanism
Dividing method, it is characterised in that: in step S1, medical image to be split is pre-processed, specifically,
S11, medical image to be split is formatted；
S12, the image after format conversion is normalized, is normalized to [0,1] section；Specifically, calculating complete
The mean value and standard deviation of portion's data images are normalized the contrast of formula manipulation image by contrast, wherein contrast normalizing
Changing formula indicates are as follows:
I=(I-Mean)/Std (1)
Wherein, I indicates the contrast of image, and Mean indicates the mean value of image data, and Std indicates the standard deviation of image data；
S13, the image after normalization is divided into training set data, verifying collection and test set data；
S14, multiscalization processing is carried out to training set data, respectively obtains the different image of sizes.
7. the medical image of the residual error type full convolutional neural networks as described in any one in claim 1-5 based on attention mechanism
Dividing method, it is characterised in that: in step S3, the training set data input full convolutional neural networks of residual error type are trained, are obtained
Convolutional neural networks model after to study；Specifically,
S31, training set data is divided into m batches, and initializes convolution kernel weight and bias；
S32, the residual error type based on attention mechanism in batches building the training set data input step S2 not being trained to
Full convolutional neural networks；
S33, it will be calculated in training set data and the full convolutional neural networks of residual error type based on attention mechanism that build,
It realizes the propagated forward of network training, exports prediction probability figure；
S34, the error corresponded between Standard Segmentation picture in each layer prediction probability figure and training set data, calculation formula are calculated
Are as follows:
It is classification corresponding to each pixel that wherein γ, which is 1.33, c,
Wherein, PicRepresent the probability that pixel i is predicted as c class, giCThe corresponding value of pixel i in Standard Segmentation image,Indicate picture
Vegetarian refreshments i is not belonging to the probability of c class,Then indicate that pixel i corresponds to 1-g in Standard Segmentation imageiCValue, N represents an image
The total number of middle pixel, the value that the value of α is 0.7, β are 0.3, ε 1；
Residual error convolution block six, residual error convolution block seven, residual error convolution block eight the corresponding error calculation function of output be FTLc, residual error
The corresponding error calculation function of the output of convolution block nine is TIc；
S35, error calculation function is minimized using momentum stochastic gradient descent method, is then carried out using the error calculation functional value
Gradient calculates, and selection multistep learning rate strategy changes learning rate when updating to network parameter, is gradually subtracted according to the number of iterations
It is small；After the completion of network parameter updates, the image of verifying collection data is input in the network model trained, calculates verifying collection number
According to the accuracy rate of middle image segmentation, if the network model after batch training concentrates accuracy rate higher than last batch training in verifying
Otherwise the accuracy rate of model, then the network model parameter after saving training are not saved into fixed disk file；Training set data it is each
Convolutional neural networks model after the completion of batch is respectively trained, after finally obtaining study.
8. the medical image segmentation side of the full convolutional neural networks of residual error type based on attention mechanism as claimed in claim 7
Method, it is characterised in that: in step S32, the different image of four kinds of sizes in training set data is inputted based on attention mechanism
The characteristic pattern of the full convolutional neural networks of residual error type is shunk in the corresponding level of network.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108021916A (en) * | 2017-12-31 | 2018-05-11 | 南京航空航天大学 | Deep learning diabetic retinopathy sorting technique based on notice mechanism |
| CN109101975A (en) * | 2018-08-20 | 2018-12-28 | 电子科技大学 | Image, semantic dividing method based on full convolutional neural networks |
| US20190057505A1 (en) * | 2017-08-17 | 2019-02-21 | Siemens Healthcare Gmbh | Automatic change detection in medical images |
-
2019
- 2019-05-28 CN CN201910454206.4A patent/CN110189334B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190057505A1 (en) * | 2017-08-17 | 2019-02-21 | Siemens Healthcare Gmbh | Automatic change detection in medical images |
| CN108021916A (en) * | 2017-12-31 | 2018-05-11 | 南京航空航天大学 | Deep learning diabetic retinopathy sorting technique based on notice mechanism |
| CN109101975A (en) * | 2018-08-20 | 2018-12-28 | 电子科技大学 | Image, semantic dividing method based on full convolutional neural networks |
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